Regional impact of aging population on carbon dioxide emissions in China: Evidence from panel threshold regression (PTR)

Carbon dioxide emission is a high-profile issue that can affect both the human economy and human existence, but few scholars have studied the relationship between these two. Therefore, this study constructs the panel threshold regression (PTR) based on the National Bureau of Statistics of China’s panel data from 2002 to 2019 in 19 regions. One of the advantages of PTR is to leverage segmented functions, allowing for a more detailed analysis of the data. Besides, by introducing the idea of a threshold, PTR can effectively avoid structural changes in the data. The different between this study and other research is that this study divides 19 regions into two parts: Eastern China and Central China. Based on that, this study researches and compares the different influences of the aging population on carbon emissions in these two regions. The results show that although the Environment Kuznets Curve has been confirmed in both Eastern China and Central China, with the development of the economy, the carbon emissions will increase in Eastern China and decrease in Central China, respectively. In addition, the key factors affecting carbon emissions in Eastern China and Central China are trade dependence and urbanization rate separately. Hence, this study suggests that the regional governments in Eastern China may guide and encourage more international trading companies to move to Central China, and at the same time, the regional governments in Central China should issue more policies to attract these companies, such as: reducing land lease fees and building better transportation infrastructure. Apart from that, the governments in Central China should vigorously increase the rate of urbanization to reduce energy consumption and improve energy efficiency.


Introduction
According to Stocker [1], the global carbon emissions in 1961 were about 9.33 billion tons and they increased to 34.65 billion tons only 50 years later.If the governments of various countries do not control carbon emissions, global carbon emissions may increase by 1 to 3 times (69.3 to Based on the above research, this study aims to probe the regional impact of the aging population on carbon dioxide emissions.To achieve this purpose, this study utilizes the panel threshold regression (PTR).PTR adopts the idea of the segmented function, which can reflect more data information which is different from the traditional model.In addition, when studying the influence of independent variables on dependent variables, independent variables are generally difficult to be directly changed.However, PTR can study how the change of the threshold variable, affects the influence of the independent variable on the dependent variable, without changing the independent variable.Therefore, this study can identify the unique threshold variable that affects different regions, so as to provide different recommendations for local governments.
The contributions of this study are as follows: first, the investigation verifies the non-linear relationship between the aging population and carbon emissions in Eastern and Central China.Second, the scrutiny fills in the gaps in the regional differences impact of the aging population on carbon emissions in the same country and provides empirical literature for the following research on the impact of the aging population on carbon emissions.Third, the exploration uses the more advanced panel threshold regression to avoid possible structural changes in the data.Fourth, the result of the study determines plausible policy suggestions for China's regions that have higher carbon emissions.
The rest of this research study is arranged as follows: Section 2 reviews the related and latest research and puts forward hypotheses; Section 3 elaborates on the methodology and data processing and sources; Section 4 presents the empirical research and discussions; and the last section presents the empirical results and policy recommendations to the Chinese government.

Literature review
Wang and Li [26] found that the aging population would reduce carbon emissions through using panel data from 154 countries.Similarly, Dalton et al. [27] also found that the aging population would reduce carbon footprints.There are two main explanations for the aging population's suppression of carbon discharge.The first is from Tonn et al. [28], that the lifestyle of the elderly tended to be a low-carbon lifestyle; Yu et al. [29] found that older people were more inclined to use public transportation.The second is the explanation from Cole and Neumayer [30], that the main reason for the increase in carbon emissions was the surge in labor supply.Sun et al. [31] found that in countries with more aging populations, lower labor force participation rates reduced economic growth, and ultimately reduced carbon dioxide emissions.However, the findings of Rjoub et al. [32] showed a positive correlation between life expectancy and carbon emissions.The root cause of the aging population is the continuous decline in birth rate and increased life expectancy.Therefore, the research of Rjoub et al. [32] surmised that the aging population will escalate greenhouse gas emissions.Fan et al. [33] divided China into two parts, urban and rural.Both the urban and rural areas showed that the aging population would significantly increase carbon emissions.They believed that the reason for this conclusion was the continuous increase in income level and consumption demand.The results of Wang and Wang [34] showed that as the population ages, different income groups and carbon footprints were correlated differently.High-income groups would increase carbon emissions, but the relationship between low-income groups and acid gas emissions was an inverted Ushape.Besides, Liu et al. [35] and Meng and Han [36] found that population density also affected carbon emissions.
The above studies prove that the population age structure has become a new factor affecting carbon dioxide emissions, but the impact of the population age structure on the emissions is inconsistent.The main reason for the inconsistencies in the findings of the previous scholars is that they carried out their studies from different perspectives.In other words, when the academics study the impact of the population age structure on carbon emissions from the perspective of population quantity, the relationship between the two is often positive.When the research framework is studied from the perspective of population quality, the relationship between the two is often negatively correlated.In the early stage of the aging population, the number of young and old people seem to increase in tandem; but the increment is greater in the number of young people compared to the old people.Therefore, the impact of population quantity is greater than that of population quality.In contrast, for the later stage of the aging population, the increase in the number of young people is lower than the increase in the number of old people.Thusly, the impact of population quality is greater than that of population quantity.Based on that, this study puts forward the following hypothesis: H1o: the relationship between the aging population and carbon emission in Eastern and Central China is linear.
H1a: the relationship between the aging population and carbon emission in Eastern and Central China is non-linear.
Kais and Sami [37] divided 58 countries into three regions and found an inverted Ushaped curve between carbon emissions and GDP per capita, which confirmed the Environmental Kuznets Curve.Li and Lin [38], correspondingly, used data from 73 countries and found that urbanization would increase and improved resource utilization, and ultimately reduced carbon emissions.However, cross-country research is difficult to help a country formulate policies to reduce carbon emissions, because the national conditions of different countries are quite different.Lenzen et al. [39] found that increasing income had different effects across countries.The research of Zhang and Zhuang [40] pointed out that the regional impact of human capital on economic growth is related to the level of economic development.Developed provinces benefit more from higher educated population, while less developed provinces rely more on people with primary and secondary education.Similarly, Wei [41] found that the initial stock of human capital had a huge and significant impact on the later accumulation of fixed assets.In fact, the initial stock of human capital in Eastern China is significantly larger than that in Central China.On that account, this study believes that the impact of human capital on different regions of China is different from one another.Zhang and Tan [42] confirmed for the first time in their research that the impact of the aging population on China's greenhouse gas present regional differences.However, when they measure the aging population, they only use the population ratio, and do not adopt a more advanced and accurate old dependency ratio.Besides that, they used the firstgeneration unit root and cointegration tests, but the first-generation tests ignore the crosssectional dependency problem.Thus, the results may be biased.Fan et al. [43] used the STIRPAT model to find that population had a large impact on CO2 emissions, especially within the proportion of the population aged 15-64.Moreover, as the level of economic development increases, the impact of per capita GDP growth on CO2 emissions showed an approximately decreasing trend.Mohmmed et al. [44] studied the ten countries with the largest carbon emissions in the world; and found that population and monetary earnings were the two major factors affecting carbon emissions in China, and the United States.However, though the income level boosted carbon emissions in some countries, it depressed them in others.Granados and Spash [45] found that unemployment depressed US carbon emissions.Xu et al. [46] found that import and export would increase China's carbon emissions.Based on this, this study puts forward the following hypothesis: H2o: the impact of the aging population in Eastern and Central China is the same.H2a: the impact of the aging population in Eastern and Central China is different.In terms of carbon emissions, the data are from Carbon Emission Account & Datasets (CEADs).The CEADs provides open, transparent, and free data downloads, aiming to provide certain research conditions and data services for academic analyzers in the field of climate change and to promote the openness and sharing of scientific research.The data were collected and calculated by the research of Shan et al. [47], Shan et al. [48], Shan et al. [49], and Guan et al. [50].

Data sources and processing
The old dependency ratio, within the research, is calculated by dividing the population over the age of 65 by the population aged 15 to 64.The calculation method of trade openness is the total amount of import and export trade divided by GDP, and trade openness is the threshold variable.The old dependency ratio is calculated by dividing the number of urban residents by the number of the total population.
Different scholars divide China's regions in different ways.For example, Zou et al. [51] and Wang et al. [52] directly divides China into two parts: urban and rural.Yet, this classification method is outdated and not suitable for the study of the aging population.It is because China already has a relatively high urbanization rate, and the urbanization rate in some developed areas is as high as 80%.There are also some scholars such as Liu et al. [53], and Meng and Han [36], who only study one region of China (Northwest and Shanghai).However, China has vast territories, and the results of one region cannot fully reflect the situation of the whole country.
In Table 2, this study refers to the classification standard of the National Bureau of Statistics of China, and divides the 19 regions into Eastern and Central divisions.There are three main reasons for this classification.First, from the perspective of economic development, since the implementation of China's Reform and Opening up in 1978, the economic development of Eastern China is significantly higher than that of Central China.Second, from the perspective of human resource, the regressive development of education in Central China leads to the quality of the labor force being generally lower than that in Eastern China; Third, from the perspective of economic structure, manufacturing and services account for a higher share of GDP in Eastern China, but Central China is still dependent on the agricultural economy.

Model specification
The stochastic impacts by regression on population, affluence, and technology model (STIR-PAT) is utilized in this study to scrutinize the impact of the aging population on carbon emissions.Ehrlich and Holdren [54] proposed IPAT model for the first time, which is often used to analyze the relationship between human activities and the environment.On this basis, Dietz and Rosa [55] extended the model to STIRPAT and the formula is as follows: Liddle and Lung [23], Fan et al. [46], and Wang and Li [26] took the logarithm on both side of the Eq 1, the formula can be rewritten as follows: In Eq 2, LNI it is the dependent variable and I represents the environmental impact which is carbon emissions in this study.LNP it is the independent variable and P represents the demographic factor of which is old dependency ratio.LNA it and LNT it are the control variable which are trade openness, GDP, square of GDP, disposable income, and population, respectively.θ it is error term, a is the constant term, i represents the regions, and t represents the time.

Cross-sectional correlation test
Cross-sectional correlation means that there may be unobserved common shocks or spillover effects between different units in the data.This problem usually occurs in data that has a strong geographical or spatial connection and is closely related to human activities.De Hoyos and Sarafidis [56] research shows that cross-sectional correlation often leads to more seriously wrong conclusions.Therefore, the text uses Pesaran [57] method to test whether there is a cross-sectional correlation problem in the data, and the formula of the test is as follows: In Eq 3, rij is the correlation coefficient between unit i and unit j, and the calculation formula is presented as follows: e it e jt ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi P T t¼1 e In Eq 4, if rij ¼ rji ¼ corrðe it ; e jt Þ ¼ 0, and i 6 ¼ j, then the null hypothesis is true.Otherwise, reject the null hypothesis.

Unit root test
There are three generations unit root tests.This study will execute the second-generation unit root test because the second-generation method can avoid across-sectional correlation issues.The second-generation method is proposed by Pesaran [58], and the formula is as follows: In Eq 5, X it represents the observed value of unit i at time t.� X t represents the mean value of all across-sectional observations at time t and � X t ¼ Each section is estimated according to Eq 5, and then the CIPS test statistic is established according to the value of the t statistic of b i obtained.The statistic in the test is established as follows:

Cointegration test
Engle and Granger [59] research first presented that if there was a cointegration relationship among variables, the model might have spurious regression issues.From the view point of this conclusion, some panel cointegration test methods are proposed.For instance, both Pedroni [60] and Persyn and Westerlund [61] proposed the panel cointegration test, separately.However, this study adopted the method of Persyn and Westerlund [61]; because this method considers about the structural breaks and the lead-lag lengths when short-duration data are employed.The specific bootstrap panel cointegration formula is as follows: In Eq 7, ΔY it is an endogenous variable, and d t refers to the deterministic component.i represents unit and t represents time.

Panel threshold regression model
This study establishes a panel threshold regression model.The threshold variable is viewed as the unknown variable in the PTR model which constructs a piecewise function and examines the corresponding threshold estimators and the effects.This econometric method was proposed by Lim et al. [62] and Hansen [63] expanded this model.Since this study assume that the relationship between the aging population and carbon emissions is non-linear, this investigation uses PTR model based on the research of Hansen [63] and the formula is as follow: where i represents the unit; t represents the time; Y it represents the dependent variable which is per capita carbon emissions; g it represents the independent variable which is the old dependency ratio; X it represents control variables which are GDP, square of GDP, disposable income, and population; β 0 is the coefficient of variables; γ represents the specific value of threshold; d it represents threshold variables which are the trade openness; and I(.) represents an indicator function.δ i represents an unpredictable factor and it has no relationship with the unit.ω it is an error term and this study assumes ω it is independent and identically distributed.
After eliminating the individual effects from each observation by subtracting the average value, Eq 8 can be written as follow: After piling the observations up, Eq 9 can be written as: Usually, to acquire the value of θ in Eq 10, ordinary least square is utilized and the function is as follows: The function of the sum of the squared residuals is: Besides, the function of the residual vector is as follows: After the parameter is obtained, there are two steps to estimate the threshold proposed by Huang et al. [64].The first step is to verify whether the model has a threshold effect, and the function is as follows: In Eq 14, the specific value of γ cannot be calculated and the distribution of the F 1 statistic is non-standard.Hansen [63] believed that progressive distribution and P-values could be estimated through bootstrap.
The second step is to verify whether the estimated threshold value and the actual value are equal.The function of likelihood ratio statistic is as follows: Empirical resultDescriptive statistic summary Table 3 indicates the outcome of the unit root test.In terms of level data of Eastern China, the trade openness, the urbanization rate, and the total population are not significant.Thus, from the perspective of level data, the variables are not stable.However, after differencing the data, all variables are significant at the 1% level.Therefore, all variables are stable in Eastern China.
For the level data of Central China, the carbon dioxide emissions, the trade openness, the urbanization rate, and the total population are insignificant.Therefore, from the perspective of level data, the variables are unstable.Nevertheless, after differencing the data, all variables are significant at the 1% level.Therefore, all variables are also stable in Central China.
Table 4 shows the outcomes of the cointegration test.Since both P values are statistically significant, there is no spurious regression among variables.Apart from that, the variables have a long-run relationship.
Table 5 illustrates that the robust threshold effect exists.During the robust threshold effect test, the threshold variable is trade openness, and the parameter of the grid is 400, bootstrap is 300, and trim is 0.01.Furthermore, both Eastern and Central China only have one flexion.population, separately.Specifically, for Eastern China, the turning point is 3.2400.For Central China, the flexion is 8.1400.Due to the data has been the Napierian Logarithm, the actual value of the turning point is 25.53% and 34.29 million people, separately.Table 7 shows the PTR outcome of Eastern and Central China.In terms of similarity between the two regions-first, since both Eastern and Central China have one turning point, respectively, the relationship between the aging population and per capita carbon emissions is non-linear.This outcome is similar with the conclusion of Yang and Wang [65].Second, because the LGDP and square term of LGDP are statistically significant at the 5% level, and the coefficient of the square term of LGDP is negative, the EKC Hypothesis is suitable in the two major regions.The difference is that Eastern China is on the left side of the curve, while Central China is on the right side.Wang et al. [52] believes that the main reason for this situation is that there is a lot of heavy industry in Eastern China.Thus, for Eastern China, relocating heavy industry or improving energy efficiency are important ways to reduce carbon emissions.Third, since the LIL of Eastern and Central China is statistically significant at the 5% level, improving educational attainment can decrease per capita carbon emissions.Research of Zafar et al. [66] and Shobande and Simplice [67] determines that education reduces carbon emissions in two main ways.First, education can improve citizens' awareness of environmental protection.Second, education can achieve sustainable green economic development.For the differences of the two regions: First, the threshold variable is different.For Eastern China, the threshold variable is trade openness, and the turning point is 3.2400 (25.53%).When the trade openness is less than 3.2400, the coefficient of LODR is -0.5925;When the trade openness is larger than 3.2400, the LODR's coefficient is -0.4188.Since both two coefficients are significant at the 1% level, from the perspective of reducing per capita carbon emissions, the government of Eastern China should keep the trade openness below 25.53%.The conclusion is the same with the research finding of Dou et al. [68].Nevertheless, for Central China, the threshold variable is the total population, and the flexion is 8.1400 (34.29 million people).When the total population is on the left side of flexion, the coefficient of LODR is 0.4066; when the total population is on the right side of flexion, the LODR's coefficient is 0.5675.However, when the LPPL is less than 8.1400 and the LODR's coefficient becomes insignificant, this study cannot determine the correlation between the aging population and per capita carbon emissions in this range.Thus, the government of Central China should keep their total population above 34.29 million people.

Results and discussion
Second, in terms of the urbanization rate, it is insignificant in Eastern China but significant in Central China.In other words, only by increasing the urbanization rate can the per capita carbon emissions in Central China be reduced.Xu [69]'s study showed that the higher the population density, the lower the per capita greenhouse gas emissions.Eastern China is highly developed and densely populated, so there is no statistical relationship between urbanization rates and carbon emissions.For Central China, increasing urbanization could make the population more concentrated, thus increasing the use of public transport and reducing carbon emissions per person.
Third, the impact of total population changes on per capita carbon emissions in Eastern China is indeterminate.Whereas increasing trade openness can effectively reduce Central China's per capita carbon emissions.
Table 8 presents robustness outcome.During the robustness test, the old dependency ratio is replaced by the percentage of elderly population.Other than that, all parameters, variables, and methods are unchanged.The robustness results show that the symbols of all variable coefficients and the values of the threshold variables do not change, except for the coefficients of variables which change slightly.The results confirm the non-linear relationship between the aging population and carbon emissions in Eastern and Central China.

Conclusions
Firstly, due to the finding that both Eastern and Central China have turning points which are statistically significant, this study rejects the first null hypothesis.In other words, the correlation between the aging population and per capita carbon emissions is non-linear.Besides, for Eastern China, the authorities there should keep the trade openness below 25.53% because it is the optimum interval, from a reducing carbon emissions perspective.Nevertheless, for Central China, governments should limit the total population to over 34.29 million.
Secondly, the EKC hypothesis is suitable in Eastern and Central China, and the turning point is 12.17, and 9.62, respectively.This outcome indicates that all of Eastern China did not arrive at the inflexion whilst the Central China was on the right side of the inflexion.However, some regions of Central China crossed the turning point in 2007, and some made the turning point crossing in 2008.Thus, for Eastern China, per capita carbon emissions will still increase with economic development, but for Central China, per capita carbon emissions will decrease with economic expansion.
Thirdly, improving educational attainment can effectively decrease per capita CO2 emissions.Improving the education level has been identified as a potential means to reduce carbon emissions for two key reasons.One, higher education levels are associated with an increased awareness of environmental protection and sustainable development among individuals.Educated individuals are more likely to understand the importance of environmental conservation and adopt environmentally friendly behaviors.For instance, they may choose to use public transportation, carpool, or engage in other low-carbon transportation options; thereby reducing carbon emissions from individual transportation activities.Two, higher education levels contribute to the development and application of sustainable technologies.Wang et al. [70] presented that intellectual people are more likely to engage in research and development activities, leading to the creation of new technologies and solutions aimed at reducing carbon footprint.For instance, research findings of Shobande [71] and Shobande and Lawrence [72] indicate that enhancing technological innovation, especially, information and communication technology can be an effective way to reduce carbon emissions.Thus, by improving education levels, societies can enhance environmental awareness, promote sustainable behaviors, and facilitate the adoption of innovative technologies.These combined efforts can contribute to a reduction in carbon emissions and support the transition towards a more sustainable and environmentally conscious society.

Policy implication
From the perspective of reducing per capita carbon emissions, Eastern China should, firstly, reduce while Central China should increase trade dependence.Hence, this study argues that the government of Eastern China should encourage some import and export trade enterprises to move to Central China.At the same time, Central China should introduce more policies to attract import and export companies to settle there, such as reducing land lease funds and building better, easily accessible transportation infrastructure.Secondly, the governments of Eastern and Central China should invest heavily in education.People who are more educated tend to have stronger awareness of protecting the environment, especially during their teenage years.In addition, these people are at ease in using public transportation.Being educated also means they are learned to manipulate high-tech machines that can improve energy efficiency, thereby reducing carbon emissions.In addition, higher educational attainment is conducive to the development and use of scientific research and technology.
Thirdly, the governments of Eastern China should increase the urbanization rate.Winter in Northern China is very cold, and the government provides central heating.This policy increases energy efficiency and reduces energy usage.However, because this arrangement requires pipes and other infrastructure, central heating is hard to come by in rural areas.Therefore, rural residents need to keep themselves warm by burning coal, wood or turning on air heater.Consequently, those methods release a large amount of carbon dioxide into the air.Hence, increasing the urbanization rate can reduce carbon emissions and improve the quality of life for people in the interior areas.
Fourth, the governments of Central China should control the total population.A larger population often correlates with higher energy consumption and increased economic activity, resulting in elevated carbon emissions.Moreover, population growth can drive changes in consumer demand and consumption patterns, potentially leading to greater resource utilization and carbon emissions.This effect is particularly noticeable when individuals prefer goods and services associated with high carbon footprints.Thus, by reducing the total population, the strain on resources and the environment can be eased, leading to a reduction in greenhouse effects and a more sustainable trajectory for the region.

Limitation
This study has several limitations.Firstly, Dou et al. [68] study indicated that import and export exercise will produce different issues in carbon emissions.Any further studies taken will carry out research on the impact of China's imports and exports of the different regions on carbon dioxide emissions respectively and consider the heterogeneity of the regions.Secondly, influenced by traditional culture, some Chinese people have a serious preference for boys over girls.Hence, the gender ratio in China is slightly unbalanced.Xinping et al. [73] finding pointed out that gender difference would have a significant impact on China's economy.Therefore, the subsequent study will explore the impact of gender differences on China's carbon emissions.In addition, this study mainly discusses the impact of demographic factors on carbon dioxide emissions.In the future research, this study will refer to the research of Makhdum et al. [74], Li et al. [75], Simionescu and Cifuentes-Faura [76], and Wang et al. [77] to discuss clean energy and its impact on carbon dioxide emission, as well as how to achieve sustainable development in the long term for China.

Fig 1
presents the analysis flowchart of PTR model.

Table 1
presents the definition and category of variables.Except for the data on carbon emissions, the rest of the data in this study are from the National Bureau of Statistics of China, and all data are taken from natural logarithms to ensure data stability.The time length of the panel data used in this study is 18 years (2002 to 2019), with the inclusion of 19 regions in China (except Hong Kong, Macau, Taiwan, and Tibet).

Table 6
represents the threshold estimator of Eastern and Central China, respectively.Besides that, the threshold variables of Eastern and Central China are trade openness and total